How many R programs distributed via Homebrew ?
What is R language ? 🔗
R is a free, open-source programming language and software environment designed primarily for statistical computing, data analysis, and graphical visualization. It is widely used by statisticians, researchers, and data scientists to manipulate data, perform complex statistical tests, and create publication-quality plots.
Origins and Core Identity 🔗
R was created in the early 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. It serves as an open-source implementation of the S programming language, which was originally developed at Bell Laboratories. Because it is released under the GNU General Public License, R is free to download and adaptable, allowing users to inspect and modify the source code.
Unlike general-purpose languages like C++ or Java, R is considered a domain-specific language (DSL) tailored specifically for data analysis and statistics. It is an interpreted language, meaning users can run code line-by-line in a command-line interface to immediately see results, rather than compiling the entire program first.
Key Features and Capabilities 🔗
R is best known for its vast ecosystem and specialized tools that streamline data science workflows.
- Comprehensive Statistical Analysis: R provides built-in support for a wide array of statistical techniques, including linear and nonlinear modeling, time-series analysis, clustering, and classification.
- Extensive Package Ecosystem: The Comprehensive R Archive Network (CRAN) hosts thousands of user-contributed packages that extend R’s functionality for specific tasks, such as bioinformatics, econometrics, or geospatial analysis.
- Advanced Visualization: R is renowned for its graphical capabilities, particularly through packages like
ggplot2, which allow users to construct complex, multi-layered data visualizations. - Cross-Platform Compatibility: The software is portable and runs seamlessly on major operating systems, including Windows, macOS, and Linux.
Comparison: R vs. Python 🔗
While both languages are dominant in data science, they serve slightly different primary purposes and philosophies.
| Feature | R Language | Python |
|---|---|---|
| Primary Focus | Statistical analysis and heavy data visualization | General-purpose programming and software development |
| Learning Curve | Steeper initially; syntax is designed for mathematicians | Generally considered easier and more intuitive for beginners |
| Libraries | specialized for complex statistics (e.g., dplyr, ggplot2) | General data science and machine learning (e.g., pandas, PyTorch) |
| Origin | Built by statisticians for statisticians | Built by programmers for general software tasks |
Common Use Cases 🔗
R is the tool of choice in academia and research-heavy industries where rigorous statistical methodology is required. It is heavily utilized in fields like bioinformatics for analyzing genomic data, in finance for quantitative risk analysis, and in pharmaceuticals for clinical trial data management. Its ability to generate reproducible research—combining code, results, and narrative in a single document using R Markdown—makes it particularly valuable for scientific publishing.
Statistics of R programming language 🔗
On December 16th 2025, The number of CLI apps written in r and distributed via Homebrew Core Formulae is 2 apps.
You may like to compare it with Python , Go , OCaml , Erlang , Rust , or Zig .
Apps written in r and distributed via Homebrew Core Formulae 🔗
- breseq : Computational pipeline for finding mutations in short-read DNA resequencing data
- jupyter-r : R support for Jupyter
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